Enterprise adoption of AI is ready to shift into higher gear. The capabilities of generative AI have captured management attention across the organization, and technology executives are moving quickly to deploy or experiment with it. Many organizations intend to increase their spending on the wider family of AI capabilities and the data infrastructure that supports them by double digits during the next year. And notwithstanding concerns about unfavorable economic conditions, executives see opportunities to leverage data and AI to deliver more growth to their organizations, to both the top and bottom lines.

Natural Language Processing (NLP) relies heavily on training data. Transformers, as they have gotten bigger, have required massive amounts of training data. To satisfy this requirement, text augmentation should be looked at as a way to expand your current dataset and to generalize your models. One text augmentation we will look at is translation augmentation. We take an English sentence and translate it to another language before translating it back to English. In this paper, we look at the effect of 108 different language back translations on various metrics and text embeddings.

Earlier this year, the EU Commission tabled a Proposal of the European Parliament and Council on the Artificial Intelligence Act ("Proposal" or the "Act") a brief summary of which can be accessed through our website. The Proposal was recently scrutinised by the European Data Protection Board ("EDPB") and the European Data Protection Supervisor ("EDPS") in a joint opinion issued on the 18th of June 2021 ("Joint Opinion"). In this Joint Opinion the EDPB and EDPS, whilst acknowledging the Commission's initiative to extend the use of Artificial Intelligence Systems ("AI Systems") throughout the Member States, rejected a few of the tabled proposals. Of particular interest, in the Joint Opinion the EDPB and EDPS delves into the interaction between the EU Data Protection Law and the provisions of the Proposal. The EDPB and EDPS highlight the importance that the two frameworks to be complementary to each other and advised that any inconsistency or conflict should be eradicated as the lack of harmonisation could lead to directly or indirectly put the fundamental right to the protection of personal data at risk.

Demand for these offerings is so high that businesses that are unable to deliver them, due to a lack of agility, are likely to become less meaningful to consumers and ultimately fall to the wayside. As organisations attempt to respond to consumers' changing requirements, artificial intelligence (AI) has been shown to be effective in helping them to provide the goods and services their customers desire. However, this technology is equally giving consumers themselves access to streamlined online tools which empower them to tailor products and services to their own personal preferences on demand. For instance, when booking a holiday, online platforms now allow consumers to build it from scratch themselves by sourcing different options for everything from flights and hotels, to car rentals and activities. While great for holidaymakers, this trend threatens the traditional package holiday and providers of those.

Machine learning has grown to have a significant impact on our daily lives: From Amazon's home assistant Alexa collecting and analyzing information to anticipate our needs, or Facebook suggesting who we should friend, to applications protecting us from credit card fraud and improving online shopping experiences. Organizations want their data to do the heavy lifting for them, driven by the desire to save on costs, improve consistency and streamline operations. While ML technologies were previously perceived as an excessive expenditure, today they are seen as an investment in the business' future and a competitive revenue driver. In order to stay competitive and successful, organizations have to invest in the right technologies and intelligently use the skills and data systems that they already have. The following three tips will help enterprises evaluate ML benefits and investments and make the most of the technology they already have.

By Benjamin Boettner Along developed riverbanks, physical barriers can help contain flooding and combat erosion. In arid regions, check dams can help retain soil after rainfall and restore damaged landscapes. In construction projects, metal plates can provide support for excavations, retaining walls on slopes, or permanent foundations. All of these applications can be addressed with the use of sheet piles, elements folded from flat material and driven vertically into the ground to form walls and stabilize soil. Proper soil stabilization is key to sustainable land management in industries such as construction, mining, and agriculture; and land degradation, the loss of ecosystem services from a given terrain, is a driver of climate change and is estimated to cost up to $10 trillion annually.

When it comes to the long-promised mass adoption of artificial intelligence (AI), many have been left wondering what exactly is holding up progress. On one hand, AI is expected to have a $13 trillion impact on the global economy by the end of the next decade. On the other, 77.1% of companies report that business adoption of AI initiatives remains a major challenge. Part of the problem is thought leaders, the media and even the public are talking about AI fervently, not pragmatically. As futurist Martin Ford writes in the introduction to his new book on the subject, Architects of Intelligence, "The result has been a sometimes incomprehensible mixture of careful, evidence-based analysis, together with hype, speculation and what might be characterized as outright fear-mongering."

Artificial Intelligence (AI), a theory where machines perform tasks with intelligence like humans, has been the talk of the town across all industries and for all the right reasons. AI is no longer just used to describe the sophisticated consumer profiling platforms and techniques used by the likes of Google and Facebook, Amazon, etc. There has been widespread investigation on AI applications, research and now live adoption in fields like Healthcare, Manufacturing or Industrial applications, Logistics, Defense & Security, Retail, and many more in order to stay competitive while also taking advantage of the additional business models to generate additional revenue. However, the challenge lies in understanding what AI means in a business context and creating a converged architecture for the specific use case that is economically viable. At Fujitsu, we observed on our digital co-creation journey with several customers that IoT implementation in industrial enterprise applications is enabling customers to utilize AI to effectively automate their processes.

We spend a lot of time and words on what autonomous cars can do, but sometimes it's a more interesting question to ask what they can't do. That's what this little bit of performance art tells me, anyway. You can see the nature of "Autonomous trap 001" right away. One of the first and most important things a self-driving system will learn or be taught is how to interpret the markings on the road. This is the edge of a lane, this means it's for carpools only, and so on.

The '90s were the era of the PC, and 2006 ushered in the era of the smartphone. Today, we are at the beginning of the third era in end-user devices: the connected car. In some ways, this shift could be even more significant than the previous ones because it combines the digital and physical world in a way we haven't seen before. As cars evolve into computers on wheels, the biggest business opportunities will be less about "metal and rubber" and more about services. McKinsey estimates that the value of connected car data could be worth $1.5 trillion a year by 2030.